





'''simple docstring'''






from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase									=   logging.get_logger(__name__)

# General docstring
lowerCAmelCase									=   """RegNetConfig"""

# Base docstring
lowerCAmelCase									=   """facebook/regnet-y-040"""
lowerCAmelCase									=   [1, 10_88, 7, 7]

# Image classification docstring
lowerCAmelCase									=   """facebook/regnet-y-040"""
lowerCAmelCase									=   """tabby, tabby cat"""

lowerCAmelCase									=   [
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 		lowerCamelCase	(   tf.keras.layers.Layer							):



	def __init__( self		,   a_		,   a_ = 3		,   a_ = 1		,   a_ = 1		,   a_ = "relu"		,   **a_		,   ):
			super().__init__(**a_						)
			# The padding and conv has been verified in
			# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
			lowerCAmelCase  :     Union[str, Any]       						= tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2						)
			lowerCAmelCase  :     Any       						= tf.keras.layers.ConvaD(
			    filters=a_		,   kernel_size=a_		,   strides=a_		,   padding="VALID"		,   groups=a_		,   use_bias=a_		,   name="convolution"		,   )
			lowerCAmelCase  :     Optional[int]       						= tf.keras.layers.BatchNormalization(epsilon=1e-5		,   momentum=0.9		,   name="normalization"						)
			lowerCAmelCase  :     List[Any]       						= ACTaFN[activation] if activation is not None else tf.identity





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Optional[Any]       						= self.convolution(self.padding(a_						)						)
			lowerCAmelCase  :     Tuple       						= self.normalization(a_						)
			lowerCAmelCase  :     Tuple       						= self.activation(a_						)
			return hidden_state






class 		lowerCamelCase	(   tf.keras.layers.Layer							):



	def __init__( self		,   a_		,   **a_						):
			super().__init__(**a_						)
			lowerCAmelCase  :     int       						= config.num_channels
			lowerCAmelCase  :     int       						= TFRegNetConvLayer(
			    out_channels=config.embedding_size		,   kernel_size=3		,   stride=2		,   activation=config.hidden_act		,   name="embedder"		,   )





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     List[str]       						= shape_list(a_						)[1]
			if tf.executing_eagerly() and num_channels != self.num_channels:
					raise ValueError(
					    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."						)

			# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
			# So change the input format from `NCHW` to `NHWC`.
			# shape = (batch_size, in_height, in_width, in_channels=num_channels)
			lowerCAmelCase  :     Union[str, Any]       						= tf.transpose(a_		,   perm=(0, 2, 3, 1)						)
			lowerCAmelCase  :     int       						= self.embedder(a_						)
			return hidden_state






class 		lowerCamelCase	(   tf.keras.layers.Layer							):



	def __init__( self		,   a_		,   a_ = 2		,   **a_						):
			super().__init__(**a_						)
			lowerCAmelCase  :     Any       						= tf.keras.layers.ConvaD(
			    filters=a_		,   kernel_size=1		,   strides=a_		,   use_bias=a_		,   name="convolution"						)
			lowerCAmelCase  :     List[Any]       						= tf.keras.layers.BatchNormalization(epsilon=1e-5		,   momentum=0.9		,   name="normalization"						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_ = False						):
			return self.normalization(self.convolution(a_						)		,   training=a_						)






class 		lowerCamelCase	(   tf.keras.layers.Layer							):



	def __init__( self		,   a_		,   a_		,   **a_						):
			super().__init__(**a_						)
			lowerCAmelCase  :     List[str]       						= tf.keras.layers.GlobalAveragePoolingaD(keepdims=a_		,   name="pooler"						)
			lowerCAmelCase  :     Optional[int]       						= [
			    tf.keras.layers.ConvaD(filters=a_		,   kernel_size=1		,   activation="relu"		,   name="attention.0"						),
			    tf.keras.layers.ConvaD(filters=a_		,   kernel_size=1		,   activation="sigmoid"		,   name="attention.2"						),
			]





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Tuple       						= self.pooler(a_						)
			for layer_module in self.attention:
					lowerCAmelCase  :     List[Any]       						= layer_module(a_						)
			lowerCAmelCase  :     Optional[int]       						= hidden_state * pooled
			return hidden_state






class 		lowerCamelCase	(   tf.keras.layers.Layer							):



	def __init__( self		,   a_		,   a_		,   a_		,   a_ = 1		,   **a_						):
			super().__init__(**a_						)
			lowerCAmelCase  :     Any       						= in_channels != out_channels or stride != 1
			lowerCAmelCase  :     Optional[int]       						= max(1		,   out_channels // config.groups_width						)
			lowerCAmelCase  :     Union[str, Any]       						= (
			    TFRegNetShortCut(a_		,   stride=a_		,   name="shortcut"						)
			    if should_apply_shortcut
			    else tf.keras.layers.Activation("linear"		,   name="shortcut"						)
			)
			# `self.layers` instead of `self.layer` because that is a reserved argument.
			lowerCAmelCase  :     Any       						= [
			    TFRegNetConvLayer(a_		,   kernel_size=1		,   activation=config.hidden_act		,   name="layer.0"						),
			    TFRegNetConvLayer(
			        a_		,   stride=a_		,   groups=a_		,   activation=config.hidden_act		,   name="layer.1"						),
			    TFRegNetConvLayer(a_		,   kernel_size=1		,   activation=a_		,   name="layer.2"						),
			]
			lowerCAmelCase  :     Tuple       						= ACTaFN[config.hidden_act]





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= hidden_state
			for layer_module in self.layers:
					lowerCAmelCase  :     Tuple       						= layer_module(a_						)
			lowerCAmelCase  :     List[Any]       						= self.shortcut(a_						)
			hidden_state += residual
			lowerCAmelCase  :     int       						= self.activation(a_						)
			return hidden_state






class 		lowerCamelCase	(   tf.keras.layers.Layer							):



	def __init__( self		,   a_		,   a_		,   a_		,   a_ = 1		,   **a_						):
			super().__init__(**a_						)
			lowerCAmelCase  :     int       						= in_channels != out_channels or stride != 1
			lowerCAmelCase  :     Dict       						= max(1		,   out_channels // config.groups_width						)
			lowerCAmelCase  :     str       						= (
			    TFRegNetShortCut(a_		,   stride=a_		,   name="shortcut"						)
			    if should_apply_shortcut
			    else tf.keras.layers.Activation("linear"		,   name="shortcut"						)
			)
			lowerCAmelCase  :     int       						= [
			    TFRegNetConvLayer(a_		,   kernel_size=1		,   activation=config.hidden_act		,   name="layer.0"						),
			    TFRegNetConvLayer(
			        a_		,   stride=a_		,   groups=a_		,   activation=config.hidden_act		,   name="layer.1"						),
			    TFRegNetSELayer(a_		,   reduced_channels=int(round(in_channels / 4						)						)		,   name="layer.2"						),
			    TFRegNetConvLayer(a_		,   kernel_size=1		,   activation=a_		,   name="layer.3"						),
			]
			lowerCAmelCase  :     Dict       						= ACTaFN[config.hidden_act]





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     List[str]       						= hidden_state
			for layer_module in self.layers:
					lowerCAmelCase  :     List[str]       						= layer_module(a_						)
			lowerCAmelCase  :     Optional[int]       						= self.shortcut(a_						)
			hidden_state += residual
			lowerCAmelCase  :     Tuple       						= self.activation(a_						)
			return hidden_state






class 		lowerCamelCase	(   tf.keras.layers.Layer							):



	def __init__( self		,   a_		,   a_		,   a_		,   a_ = 2		,   a_ = 2		,   **a_						):
			super().__init__(**a_						)

			lowerCAmelCase  :     List[str]       						= TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
			lowerCAmelCase  :     int       						= [
			    # downsampling is done in the first layer with stride of 2
			    layer(a_		,   a_		,   a_		,   stride=a_		,   name="layers.0"						),
			    *[layer(a_		,   a_		,   a_		,   name=F'''layers.{i+1}'''						) for i in range(depth - 1						)],
			]





	def 	_lowerCamelCase     ( self		,   a_						):


			for layer_module in self.layers:
					lowerCAmelCase  :     Optional[int]       						= layer_module(a_						)
			return hidden_state






class 		lowerCamelCase	(   tf.keras.layers.Layer							):



	def __init__( self		,   a_		,   **a_						):
			super().__init__(**a_						)
			lowerCAmelCase  :     Union[str, Any]       						= []
			# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
			self.stages.append(
			    TFRegNetStage(
			        a_		,   config.embedding_size		,   config.hidden_sizes[0]		,   stride=2 if config.downsample_in_first_stage else 1		,   depth=config.depths[0]		,   name="stages.0"		,   )						)
			lowerCAmelCase  :     Optional[int]       						= zip(config.hidden_sizes		,   config.hidden_sizes[1:]						)
			for i, ((in_channels, out_channels), depth) in enumerate(zip(a_		,   config.depths[1:]						)						):
					self.stages.append(TFRegNetStage(a_		,   a_		,   a_		,   depth=a_		,   name=F'''stages.{i+1}'''						)						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_ = False		,   a_ = True						):
			lowerCAmelCase  :     Tuple       						= () if output_hidden_states else None

			for stage_module in self.stages:
					if output_hidden_states:
							lowerCAmelCase  :     str       						= hidden_states + (hidden_state,)

					lowerCAmelCase  :     Dict       						= stage_module(a_						)

			if output_hidden_states:
					lowerCAmelCase  :     Any       						= hidden_states + (hidden_state,)

			if not return_dict:
					return tuple(v for v in [hidden_state, hidden_states] if v is not None						)

			return TFBaseModelOutputWithNoAttention(last_hidden_state=a_		,   hidden_states=a_						)






@keras_serializable
class 		lowerCamelCase	(   tf.keras.layers.Layer							):
	snake_case_   = RegNetConfig



	def __init__( self		,   a_		,   **a_						):
			super().__init__(**a_						)
			lowerCAmelCase  :     str       						= config
			lowerCAmelCase  :     Union[str, Any]       						= TFRegNetEmbeddings(a_		,   name="embedder"						)
			lowerCAmelCase  :     str       						= TFRegNetEncoder(a_		,   name="encoder"						)
			lowerCAmelCase  :     Optional[Any]       						= tf.keras.layers.GlobalAveragePoolingaD(keepdims=a_		,   name="pooler"						)





	@unpack_inputs
	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = False		,   ):
			lowerCAmelCase  :     Optional[int]       						= (
			    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
			)
			lowerCAmelCase  :     int       						= return_dict if return_dict is not None else self.config.use_return_dict

			lowerCAmelCase  :     List[str]       						= self.embedder(a_		,   training=a_						)

			lowerCAmelCase  :     Any       						= self.encoder(
			    a_		,   output_hidden_states=a_		,   return_dict=a_		,   training=a_						)

			lowerCAmelCase  :     Dict       						= encoder_outputs[0]
			lowerCAmelCase  :     int       						= self.pooler(a_						)

			# Change to NCHW output format have uniformity in the modules
			lowerCAmelCase  :     str       						= tf.transpose(a_		,   perm=(0, 3, 1, 2)						)
			lowerCAmelCase  :     Optional[Any]       						= tf.transpose(a_		,   perm=(0, 3, 1, 2)						)

			# Change the other hidden state outputs to NCHW as well
			if output_hidden_states:
					lowerCAmelCase  :     List[str]       						= tuple([tf.transpose(a_		,   perm=(0, 3, 1, 2)						) for h in encoder_outputs[1]]						)

			if not return_dict:
					return (last_hidden_state, pooled_output) + encoder_outputs[1:]

			return TFBaseModelOutputWithPoolingAndNoAttention(
			    last_hidden_state=a_		,   pooler_output=a_		,   hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states		,   )






class 		lowerCamelCase	(   _A							):
	snake_case_   = RegNetConfig
	snake_case_   = '''regnet'''
	snake_case_   = '''pixel_values'''



	@property
	def 	_lowerCamelCase     ( self						):
			return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)		,   dtype=tf.floataa						)}






lowerCAmelCase									=   R"""
    Parameters:
    This model is a Tensorflow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
    behavior.
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCAmelCase									=   R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."   , _A   , )
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   *a_		,   **a_						):
			super().__init__(a_		,   *a_		,   **a_						)
			lowerCAmelCase  :     Union[str, Any]       						= TFRegNetMainLayer(a_		,   name="regnet"						)





	@unpack_inputs
	@add_start_docstrings_to_model_forward(a_						)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC		,   output_type=a_		,   config_class=_CONFIG_FOR_DOC		,   modality="vision"		,   expected_output=_EXPECTED_OUTPUT_SHAPE		,   )
	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   a_=False		,   ):
			lowerCAmelCase  :     List[str]       						= (
			    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
			)
			lowerCAmelCase  :     List[Any]       						= return_dict if return_dict is not None else self.config.use_return_dict

			lowerCAmelCase  :     Dict       						= self.regnet(
			    pixel_values=a_		,   output_hidden_states=a_		,   return_dict=a_		,   training=a_		,   )
			if not return_dict:
					return (outputs[0],) + outputs[1:]

			return TFBaseModelOutputWithPoolingAndNoAttention(
			    last_hidden_state=outputs.last_hidden_state		,   pooler_output=outputs.pooler_output		,   hidden_states=outputs.hidden_states		,   )






@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "   , _A   , )
class 		lowerCamelCase	(   _A   , _A							):



	def __init__( self		,   a_		,   *a_		,   **a_						):
			super().__init__(a_		,   *a_		,   **a_						)
			lowerCAmelCase  :     Optional[Any]       						= config.num_labels
			lowerCAmelCase  :     Optional[int]       						= TFRegNetMainLayer(a_		,   name="regnet"						)
			# classification head
			lowerCAmelCase  :     List[str]       						= [
			    tf.keras.layers.Flatten(),
			    tf.keras.layers.Dense(config.num_labels		,   name="classifier.1"						) if config.num_labels > 0 else tf.identity,
			]





	@unpack_inputs
	@add_start_docstrings_to_model_forward(a_						)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT		,   output_type=a_		,   config_class=_CONFIG_FOR_DOC		,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,   )
	def 	_lowerCamelCase     ( self		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_=False		,   ):
			lowerCAmelCase  :     Any       						= (
			    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
			)
			lowerCAmelCase  :     List[str]       						= return_dict if return_dict is not None else self.config.use_return_dict

			lowerCAmelCase  :     Any       						= self.regnet(
			    a_		,   output_hidden_states=a_		,   return_dict=a_		,   training=a_						)

			lowerCAmelCase  :     int       						= outputs.pooler_output if return_dict else outputs[1]

			lowerCAmelCase  :     str       						= self.classifier[0](a_						)
			lowerCAmelCase  :     List[str]       						= self.classifier[1](a_						)

			lowerCAmelCase  :     List[str]       						= None if labels is None else self.hf_compute_loss(labels=a_		,   logits=a_						)

			if not return_dict:
					lowerCAmelCase  :     Optional[Any]       						= (logits,) + outputs[2:]
					return ((loss,) + output) if loss is not None else output

			return TFSequenceClassifierOutput(loss=a_		,   logits=a_		,   hidden_states=outputs.hidden_states						)









'''simple docstring'''







import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						).convert('RGB'						)

   return image







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []
   # fmt: off

   # vision encoder
   rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')						)
   rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')						)
   rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')						)
   rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')						)
   rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')						)
   rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')						)

   for i in range(config.vision_config.num_hidden_layers						):
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")						)

   # QFormer
   rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')						)
   rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')						)

   # fmt: on
   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   for i in range(config.vision_config.num_hidden_layers						):
      # read in original q and v biases
      A__       =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""						)
      A__       =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""						)

      # next, set bias in the state dict
      A__       =     torch.cat((q_bias, torch.zeros_like(UpperCAmelCase      ,requires_grad=UpperCAmelCase						), v_bias)						)
      A__       =     qkv_bias







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     364 if 'coco' in model_name else 224
   A__       =     InstructBlipVisionConfig(image_size=UpperCAmelCase						).to_dict()

   # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
   # seems like flan-T5 models don't have bos_token_id properly set?
   if "t5-xl" in model_name:
      A__       =     TaConfig.from_pretrained('google/flan-t5-xl'      ,dense_act_fn='gelu'      ,bos_token_id=1						).to_dict()
   elif "t5-xxl" in model_name:
      A__       =     TaConfig.from_pretrained('google/flan-t5-xxl'      ,dense_act_fn='gelu'      ,bos_token_id=1						).to_dict()
   elif "vicuna-7b" in model_name:
      A__       =     LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'      ,vocab_size=32001						).to_dict()
   elif "vicuna-13b" in model_name:
      A__       =     LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'      ,vocab_size=32001						).to_dict()
   else:
      raise ValueError('Model name not supported'						)

   # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
   A__       =     InstructBlipQFormerConfig(vocab_size=30523						).to_dict()
   A__       =     InstructBlipConfig(vision_config=UpperCAmelCase      ,text_config=UpperCAmelCase      ,qformer_config=UpperCAmelCase						)

   return config, image_size







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=None      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     AutoTokenizer.from_pretrained('bert-base-uncased'      ,truncation_side='left'						)
   qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}						)

   if "t5" in model_name:
      A__       =     TaTokenizerFast.from_pretrained('google/flan-t5-xl'      ,truncation_side='left'						)
   elif "vicuna" in model_name:
      # the following was used in the original implementation:
      # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
      # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
      # tokenizer.add_special_tokens({"bos_token": "</s>"})
      # tokenizer.add_special_tokens({"eos_token": "</s>"})
      # tokenizer.add_special_tokens({"unk_token": "</s>"})
      A__       =     LlamaTokenizerFast.from_pretrained(
          'huggyllama/llama-7b'      ,truncation_side='left'      ,bos_token='</s>'      ,unk_token='</s>'						)
      tokenizer.add_special_tokens({'pad_token': '[PAD]'}						)

   A__  ,			A__       =     get_blipa_config(UpperCAmelCase						)
   A__       =     InstructBlipForConditionalGeneration(UpperCAmelCase						).eval()

   A__       =     {
       'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
       'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
       'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
       'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
   }

   A__  ,			A__       =     model_name_to_original[model_name]

   # load original model
   print('Loading original model...'						)
   A__       =     'cuda:1' if torch.cuda.is_available() else 'cpu'
   A__       =     'cuda:2' if torch.cuda.is_available() else 'cpu'
   A__  ,			A__  ,			A__       =     load_model_and_preprocess(
       name=UpperCAmelCase      ,model_type=UpperCAmelCase      ,is_eval=UpperCAmelCase      ,device=UpperCAmelCase						)
   original_model.eval()
   print('Done!'						)

   # update state dict keys
   A__       =     original_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      A__       =     state_dict.pop(UpperCAmelCase						)
      if key.startswith('Qformer.bert'						):
         A__       =     key.replace('Qformer.bert'      ,'qformer'						)
      if "attention.self" in key:
         A__       =     key.replace('self'      ,'attention'						)
      if "llm_proj" in key:
         A__       =     key.replace('llm_proj'      ,'language_projection'						)
      if "t5_proj" in key:
         A__       =     key.replace('t5_proj'      ,'language_projection'						)
      if key.startswith('llm_model'						):
         A__       =     key.replace('llm_model'      ,'language_model'						)
      if key.startswith('t5'						):
         A__       =     key.replace('t5'      ,'language'						)
      A__       =     val

   # read in qv biases
   read_in_q_v_bias(UpperCAmelCase      ,UpperCAmelCase						)

   # note: weights get loaded in torch.float32 by default
   hf_model.load_state_dict(UpperCAmelCase      ,strict=UpperCAmelCase						)

   A__       =     load_demo_image()
   A__       =     'What is unusual about this image?'

   # create processor
   A__       =     BlipImageProcessor(
       size={'height': image_size, 'width': image_size}      ,image_mean=UpperCAmelCase      ,image_std=UpperCAmelCase						)
   A__       =     InstructBlipProcessor(
       image_processor=UpperCAmelCase      ,tokenizer=UpperCAmelCase      ,qformer_tokenizer=UpperCAmelCase      ,)
   A__       =     processor(images=UpperCAmelCase      ,text=UpperCAmelCase      ,return_tensors='pt'						).to(UpperCAmelCase						)

   # make sure processor creates exact same pixel values
   A__       =     vis_processors['eval'](UpperCAmelCase						).unsqueeze(0						).to(UpperCAmelCase						)
   A__       =     inputs.pixel_values
   assert torch.allclose(original_pixel_values.to(pixel_values.device						)      ,UpperCAmelCase						)

   original_model.to(UpperCAmelCase						)
   hf_model.to(UpperCAmelCase						)
   with torch.no_grad():
      if "vicuna" in model_name:
         A__       =     original_model({'image': original_pixel_values, 'text_input': [prompt]}						).logits
         A__       =     hf_model(**UpperCAmelCase						).logits
      else:
         A__       =     original_model(
             {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}						).logits
         A__       =     tokenizer('\n'      ,return_tensors='pt'						).input_ids.to(UpperCAmelCase						)
         A__       =     label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id      ,-100						)
         A__       =     hf_model(**UpperCAmelCase      ,labels=UpperCAmelCase						).logits

   print('First values of original logits:'      ,original_logits[0, :3, :3]						)
   print('First values of HF logits:'      ,logits[0, :3, :3]						)

   # assert values
   assert original_logits.shape == logits.shape
   A__       =     1e-4 if 'vicuna' in model_name else 1e-5
   assert torch.allclose(original_logits.to(logits.device						)      ,UpperCAmelCase      ,atol=UpperCAmelCase						)
   print('Looks ok!'						)

   print('Generating with original model...'						)
   A__       =     original_model.generate({'image': original_pixel_values, 'prompt': prompt}      ,num_beams=5						)

   # important: we need to cast the weights of the HF model to the appropriate type
   print('Generating with HF model...'						)
   A__       =     hf_model.generate(
       **UpperCAmelCase      ,do_sample=UpperCAmelCase      ,num_beams=5      ,max_length=256      ,min_length=1      ,top_p=0.9      ,repetition_penalty=1.5      ,length_penalty=1.0      ,temperature=1      ,)
   if "vicuna" in model_name:
      # convert output id 0 to 2 (eos_token_id)
      # TODO add this in the generate method?
      A__       =     2
   print('Original generation:'      ,UpperCAmelCase						)
   A__       =     processor.batch_decode(UpperCAmelCase      ,skip_special_tokens=UpperCAmelCase						)
   A__       =     [text.strip() for text in output_text]
   print('HF generation:'      ,UpperCAmelCase						)

   if pytorch_dump_folder_path is not None:
      processor.save_pretrained(UpperCAmelCase						)
      hf_model.save_pretrained(UpperCAmelCase						)

   if push_to_hub:
      processor.push_to_hub(F"""Salesforce/{model_name}"""						)
      hf_model.push_to_hub(F"""Salesforce/{model_name}"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    lowerCAmelCase_     						=				[
        '''instructblip-vicuna-7b''',
        '''instructblip-vicuna-13b''',
        '''instructblip-flan-t5-xl''',
        '''instructblip-flan-t5-xxl''',
    ]
    parser.add_argument(
        '''--model_name''',
        default='''instructblip-flan-t5-xl''',
        choices=choices,
        type=str,
        help='''Path to hf config.json of model to convert''',
    )
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--push_to_hub''',
        action='''store_true''',
        help='''Whether to push the model and processor to the hub after converting''',
    )

    lowerCAmelCase_     						=				parser.parse_args()

    convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


